We present a framework for using continuous-space vector representations of word meaning to derive representations of the meaning of word senses listed in a semantic network. The idea is based on two assumptions: 1) word vectors for polysemous words are a mix of underlying sense representations; 2) the representation of a sense should be similar to those of its neighbors in the network. This leads to a constrained optimization problem, and we present anapproximate iterative algorithm that can be used if the similarity between senses is defined in terms of the squared Euclidean distance.

We apply the algorithm on a Swedish semantic network, and we evaluate the quality of the resulting sense representations intrinsically using the vector offset method and extrinsically by showing that they give large improvements when used in classifiers that maps word senses to FrameNet frames.